How does a chatbot built on the Kore.ai Platform turn natural language into execution of a task?

The Kore.ai Platform uses fundamental meaning combined with ML algorithms to parse user input, recognize intent, and extract necessary entities in order to complete a task. Intent recognition consists of determining what part of the user’s utterance (or request) is considered the task. A chatbot task can be broken down into a few words that describe what the user intends to do, usually a verb and a noun: Find an ATM, Create an event, etc.

Intent Recognition

Our NLP engine analyzes the structure of a user’s command to identify each word by meaning, position, conjugation, capitalization, plurality, and other factors. This helps the chatbot correctly interpret and understand obvious and non-obvious synonyms for these common “action” words.

The goal of intent recognition isn’t just to match an utterance with a task, it’s to match an utterance with its correctly intended task. We do this by matching verbs and nouns with as many obvious and non-obvious synonyms as possible.

Entity Extraction

Entities are the fields, data, or words the developer designates necessary for the chatbot to complete a task: a date, time, person, location, description of an item or a product, or any number of other designations

Through our NLP engine, the bot identifies words from a user’s utterance to ensure all available fields match the task at hand, or collects additional field data if needed.

The goal of entity extraction is to fill any holes needed to complete the task, while ignoring unneeded details. It’s a subtractive process to get just the necessary info – whether the user provides all at once, or through a guided conversation with the chatbot.